1. Field of the Invention
The present invention relates in general to the field of statistical process control (SPC) and more specifically, to real-time analysis of SPC metrics.
2. Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
Business and personal consumers alike are becoming more sophisticated. They demand the best quality at the lowest possible price, and many times, desire products that are custom-tailored to meet their needs. This has resulted in the growing popularity of mass customization, which has become especially popular when purchasing products such as user-configured information handling systems. At the same time, these consumers expect exemplary support and service, which can be challenging to deliver at marginal price points. Known approaches to achieving this goal include the use of stringent quality controls and performance monitoring of business processes.
In the past, quality control was achieved through inspection. Finished products were individually accepted or rejected based on their adherence to predetermined metrics. In contrast, statistical process control (SPC) uses statistical analysis to observe the performance of a process or production line to predict deviations that could result in unacceptable products or service. At its simplest, SPC uses statistical tools such as mean and variance to determine whether an observed process is performing within acceptable parameters. The underlying assumption is that any production process will result in slight variations, even when the process is running normally, and these variances can be statistically analyzed to maintain control of the process. These statistical analyses are typically depicted as graphical plots in a variety of control chart formats to facilitate human monitoring of the observed process.
Implementation of SPC can result in large volumes of process data being generated, many times across multiple systems. In many cases, this data is iteratively collected and aggregated into a centralized data store over a predetermined period of time. Then the data corresponding to desired metrics is extracted and provided as input to a SPC analysis system to identify trends, deviations or patterns. The results of the analysis are then reviewed and corrective actions taken if required. However, the collection, extraction and conversion of process control data for analysis often involve manual processes which can be lengthy, time consuming and inefficient. Furthermore, in many cases process control data is not available in real-time nor is all required information available concurrently. This lag in information availability can cause reactive decisions that are made based on lagging indicators (e.g., historical data and trends) rather than leading indicators. Predictive modeling of SPC analysis results is likewise impeded, which can hamper proactive efforts and make it difficult to determine the cause of quality issues. As a result, the inability to concurrently acquire process control data in real-time offsets the benefits of SPC analyses.